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Enhancing Micro-location Accuracy for Asset Tracking: An Evaluation of Two Fingerprinting Approaches Using Three Machine Learning Algorithms

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Proceedings of the Future Technologies Conference (FTC) 2021, Volume 2 (FTC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 359))

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Abstract

Micro-location is the ability to track a user’s location or asset at a granular level and is the next evolution in location services. However, this evolution is not proceeding without challenges. Current mobile device GPS systems are not accurate enough to provide the granularity required; especially indoors when there is no line-of-sight with satellites. One promising solution involves using Bluetooth Low Energy beacons. In this paper, we present an indoor location system that uses BLE beacons and iPhones. We present the relevant background of work in this area, the architectural framework that we designed and developed, the mobile apps, the machine learning algorithms employed and report on the findings of two fingerprinting methods using three machine learning algorithms. Our indoor micro-location prediction system using beacons consistently performs at an accuracy of at least 1.7 m spherical radius from the target with convergence in under 2 s and was validated on 2,000 trials.

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Authors and Affiliations

  1. Sheridan College, Oakville, ON, Canada

    Edward R. Sykes & Abdul Mustafa

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  1. Edward R. Sykes
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  2. Abdul Mustafa
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Correspondence to Edward R. Sykes .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Sykes, E.R., Mustafa, A. (2022). Enhancing Micro-location Accuracy for Asset Tracking: An Evaluation of Two Fingerprinting Approaches Using Three Machine Learning Algorithms. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 2. FTC 2021. Lecture Notes in Networks and Systems, vol 359. Springer, Cham. https://doi.org/10.1007/978-3-030-89880-9_7

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